[AMD] [MiniMax-M2.5 Day 0] Add MiniMax-M2.5 nightly accuracy test (#19443)

docs/basic_usage/minimax_m2.md

@@ -1,13 +1,14 @@
-# MiniMax M2.1/M2 Usage
+# MiniMax M2.5/M2.1/M2 Usage
 
-[MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1) and [MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) are advanced large language models created by [MiniMax](https://www.minimax.io/).
+[MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5), [MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1), and [MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) are advanced large language models created by [MiniMax](https://www.minimax.io/).
 
-MiniMax-M2 series redefines efficiency for agents. It's a compact, fast, and cost-effective MoE model (230 billion total parameters with 10 billion active parameters) built for elite performance in coding and agentic tasks, all while maintaining powerful general intelligence. With just 10 billion activated parameters, MiniMax-M2 provides the sophisticated, end-to-end tool use performance expected from today's leading models, but in a streamlined form factor that makes deployment and scaling easier than ever.
+The MiniMax-M2 series redefines efficiency for agents. These compact, fast, and cost-effective MoE models (230 billion total parameters with 10 billion active parameters) are built for elite performance in coding and agentic tasks, all while maintaining powerful general intelligence. With just 10 billion activated parameters, the MiniMax-M2 series provides sophisticated, end-to-end tool use performance expected from today's leading models, but in a streamlined form factor that makes deployment and scaling easier than ever.
 
 ## Supported Models
 
 This guide applies to the following models. You only need to update the model name during deployment. The following examples use **MiniMax-M2**:
 
+- [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5)
 - [MiniMaxAI/MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1)
 - [MiniMaxAI/MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2)
 
@@ -49,6 +50,24 @@ python -m sglang.launch_server \
     --mem-fraction-static 0.85
 ```
 
+### AMD GPUs (MI300X/MI325X/MI355X)
+
+8-GPU deployment command:
+
+```bash
+SGLANG_USE_AITER=1 python -m sglang.launch_server \
+    --model-path MiniMaxAI/MiniMax-M2.5 \
+    --tp-size 8 \
+    --ep-size 8 \
+    --attention-backend aiter \
+    --tool-call-parser minimax-m2 \
+    --reasoning-parser minimax-append-think \
+    --host 0.0.0.0 \
+    --trust-remote-code \
+    --port 8000 \
+    --mem-fraction-static 0.85
+```
+
 ## Testing Deployment
 
 After startup, you can test the SGLang OpenAI-compatible API with the following command:

docs/supported_models/text_generation/generative_models.md

@@ -37,7 +37,7 @@ in the GitHub search bar.
 | **MiniCPM** (v3, 4B)               | `openbmb/MiniCPM3-4B`                            | OpenBMB’s series of compact LLMs for edge devices; MiniCPM 3 (4B) achieves GPT-3.5-level results in text tasks. |
 | **OLMo** (2, 3) | `allenai/OLMo-3-1125-32B`, `allenai/OLMo-3-32B-Think`, `allenai/OLMo-2-1124-7B-Instruct` | Allen AI’s series of Open Language Models designed to enable the science of language models. |
 | **OLMoE** (Open MoE)               | `allenai/OLMoE-1B-7B-0924`                       | Allen AI’s open Mixture-of-Experts model (7B total, 1B active parameters) delivering state-of-the-art results with sparse expert activation. |
-| **MiniMax-M2** (M2, M2.1)                     | `minimax/MiniMax-M2`, `minimax/MiniMax-M2.1`           | MiniMax’s SOTA LLM for coding & agentic workflows. |
+| **MiniMax-M2** (M2, M2.1, M2.5)               | `MiniMaxAI/MiniMax-M2.5`, `MiniMaxAI/MiniMax-M2.1`, `MiniMaxAI/MiniMax-M2` | MiniMax's SOTA LLM for coding & agentic workflows. |
 | **StableLM** (3B, 7B)               | `stabilityai/stablelm-tuned-alpha-7b`            | StabilityAI’s early open-source LLM (3B & 7B) for general text generation; a demonstration model with basic instruction-following ability. |
 | **Command-(R,A)** (Cohere)              | `CohereLabs/c4ai-command-r-v01`, `CohereLabs/c4ai-command-r7b-12-2024`, `CohereLabs/c4ai-command-a-03-2025`                 | Cohere’s open conversational LLM (Command series) optimized for long context, retrieval-augmented generation, and tool use. |
 | **DBRX** (Databricks)              | `databricks/dbrx-instruct`                       | Databricks’ 132B-parameter MoE model (36B active) trained on 12T tokens; competes with GPT-3.5 quality as a fully open foundation model. |